distributed-quantum-computing

name	distributed-quantum-computing
description	Distributed Quantum Computing architecture and patterns. Apply when designing multi-QPU systems, quantum communication protocols, or scaling quantum computing beyond single device limitations.
metadata	{"openclaw":{"emoji":"⏛","source":"arxiv:2212.10609,arxiv:2404.01265","authors":["Caleffi et al.","Barral et al."],"year":2024}}

Distributed Quantum Computing

Framework from arxiv:2212.10609 & arxiv:2404.01265 - scaling quantum computing via distributed paradigm.

Core Problem

Single QPU Limitation:

• Current: ~100-1000 noisy qubits
• Need: ~10,000-1,000,000 noise-free qubits for practical quantum advantage
• Solution: Distributed quantum computing - multiple QPUs communicating and cooperating

Architecture

┌─────────────────────────────────────────────────────────────┐
│                 Distributed Quantum Computing System         │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│   ┌──────────┐    ┌──────────┐    ┌──────────┐              │
│   │  QPU-1   │    │  QPU-2   │    │  QPU-3   │              │
│   │ 100 qubits│    │ 100 qubits│    │ 100 qubits│             │
│   └──────────┘    ┌──────────┘    └──────────┘              │
│        │              │              │                       │
│        └──────────────┼──────────────┘                       │
│                       │                                      │
│               ┌───────▼───────┐                              │
│               │ Quantum Network│                              │
│               │  (Entanglement │                              │
│               │   Distribution)│                              │
│               └───────┬───────┘                              │
│                       │                                      │
│               ┌───────▼───────┐                              │
│               │  Distributed  │                              │
│               │  Quantum Gates│                              │
│               └───────┬───────┘                              │
│                       │                                      │
│               ┌───────▼───────┐                              │
│               │   Scheduler   │                              │
│               │  (Task Distribution)│                         │
│               └───────┴───────┘                              │
│                                                              │
└─────────────────────────────────────────────────────────────┘

Key Components

1. Quantum Communication Protocols

Protocol	Purpose
Teleportation	Transfer quantum state between QPUs
Entanglement swapping	Create entanglement across non-directly connected QPUs
Quantum routing	Route qubits through quantum network

2. Distributed Quantum Gates

• Non-local gates: Gates acting on qubits across different QPUs
• CAT gates: Communication-Assisted Teleportation gates
• Telegate protocol: Teleport gate execution to remote QPU

3. Scheduler

• Task decomposition across QPUs
• Minimize communication overhead
• Balance QPU workload

Challenges

Challenge	Description	Current Solutions
Entanglement distribution	Create/maintain entanglement across QPUs	Quantum repeaters, entanglement swapping
Noise propagation	Errors spread across distributed system	Distributed error correction
Communication overhead	Teleportation requires classical communication	Minimize non-local gates
Synchronization	QPUs must be synchronized	Distributed quantum clock
Scalability	Network topology limits scaling	Hierarchical architecture

Design Patterns

Pattern 1: Quantum Circuit Partitioning

def partition_circuit(circuit, n_qpus):
    """Partition quantum circuit across multiple QPUs."""
    partitions = []
    for i in range(n_qpus):
        partition = extract_local_gates(circuit, qpu_range=i)
        non_local_gates = extract_non_local_gates(circuit, qpu_range=i)
        partitions.append({
            'local': partition,
            'non_local': non_local_gates,
            'communication': estimate_teleportation_cost(non_local_gates)
        })
    return optimize_partition(partitions)

Pattern 2: Entanglement Distribution Network

QPUs connected via quantum network:
- Direct links: High-fidelity entanglement
- Indirect links: Entanglement swapping via repeaters
- Topology: Minimize shortest path between any two QPUs

Federated Quantum Learning

• See q-anchor-federated-quantum-learning skill for QFL with ZNE-guided correction addressing double-drift (client drift + hardware bias)

Key Pattern: Distributed Quantum Error Correction

• Surface code adapted for distributed QPUs
• Parity checks across QPU boundaries
• Requires entangled ancilla qubits

Metrics

Metric	Target
Entanglement fidelity	> 0.99 for distributed gates
Communication latency	< 1ms for teleportation
QPU utilization	> 80% parallel execution
Error rate	< 0.001 per distributed operation

Applications

• Distributed Shor's algorithm: Factor large numbers across QPUs
• Distributed QAOA: Optimize large-scale optimization problems
• Quantum simulation: Simulate larger quantum systems
• Quantum machine learning: Train larger quantum models

Relation to OpenClaw

OpenClaw's distributed agent architecture parallels DQC:

• Multiple QPUs → Multiple agent instances
• Quantum communication → Agent communication protocols
• Distributed gates → Cross-agent tool calls
• Scheduler → Agent orchestrator

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Sources: arxiv:2212.10609 (Caleffi et al., 2024), arxiv:2404.01265 (Barral et al., 2024)